Context

This is the processed version of this dataset: https://www.kaggle.com/brendaso/2019-coronavirus-dataset-01212020-01262020 I have filled NAs with 0, added longitude and latitude columns for easier geospatial analysis.

Description

Dive into the dynamic narrative of the 'Global Covid Trend' dataset, capturing the ebb and flow of COVID-19 cases worldwide. Uncover trends, patterns, and vital statistics meticulously compiled to paint a comprehensive picture of the pandemic's journey across the globe.

Key Features

• Province/State: Province or state of the location
• Country/Region: Country or region of the location
• Lat: Latitude coordinate
• Long: Longitude coordinate
• 1/22/2020 - 1/2/2022: Daily COVID-19 cases from January 22, 2020, to February 2, 2022

Sample Data

Dataset Information

• Data Format: CSV (Comma-Separated Values)

It's important to note that the data does not contain any personally identifiable information (PII) to ensure privacy and comply with ethical guidelines.

If this was helpful, a vote is appreciated 🙂❤️

During the first two year of the Covid-19 pandemic, deaths tolls differed from a country to another. In a previous research work on 39 countries, we have found that some population’s characteristics were either negatively (birth rate/mortality rate, fertility rate) or positively (cancer score, Alzheimer disease score, percent of people above 65 years old, levels of alcohol intake) correlated with Covid-19 mortality. We also found that low levels of climate factors (average annual temperature, average hours of sunshine, average annual level of UV index) were positively correlated with Covid-19 deaths numbers as well. In the present study, we have developped an anti-Covid Capacity index that takes into account all the above mentioned parameters. The polynomial analysis of the anti-Covid Capacity and its corresponding geographic latitude of each country has generated a bell-shaped curve, with a high coefficient of determination (R2= 0.78). Lower anti-Covid capacity values were recorded in countries of low and high latitudes, respectively. Instead, plotting covid-19 deaths numbers against geographic latitude levels has generated an inverted bell-shaped curve, with higher deaths numbers at low and high latitudes, respectively. The analysis by a simple linear regression has shown that Covid-19 deaths numbers were significantly (p= 2,40 x 10-9) and negatively correlated to the anti-Covid Capacity index values. Our data demonstrate that the negative prepandemic human conditions, and the low scores of both annual temperature and UV index in many countries were the key factors behind high Covid-19 mortality, and they can be expressed as a simple index of anti-Covid capacity of a country that can predict the death-associated severity of Covid-19 disease, and thus, according to a country’s geographic latitude.

Context

The 2019–20 coronavirus pandemic is an ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Source: https://en.wikipedia.org/wiki/2019%E2%80%9320_coronavirus_pandemic.

Content

Coronavirus COVID-19 confirmed cases, deaths, case mortality ratios, country, latitude, and longitude.

Disclaimer: Data will be more accurate as more data comes in. Deaths/Infections will be a better measure of mortality rate after a pandemic is over, when the estimates of the number of infections start to get closer to the true number of infected individuals. Note discussion of case mortality ratio (numbers as they are reported) vs infection mortality ratio (estimates of the actual numbers). This dataset discusses case mortality ratios.

Acknowledgements

Banner photo by Adhy Savala on Unsplash.

Data generated from the notebook https://www.kaggle.com/paultimothymooney/does-latitude-impact-the-spread-of-covid-19 using data from https://www.kaggle.com/paultimothymooney/latitude-and-longitude-for-every-country-and-state and https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset, all of which were released under open data licenses.

"COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries"

Dataset for preprint titled "COVID-19 mortality: positive correlation with cloudiness but no correlation with sunlight and latitude in Europe" https://doi.org/10.1101/2021.01.27.21250658

by SECIL OMER, ADRIAN IFTIME, VICTOR BURCEA

Corresponding author: A. Iftime, University of Medicine and Pharmacy "Carol Davila", Biophysics Department, 8 Blvd. Eroii Sanitari, 050474 Bucharest, Romania. Email address: adrian.iftime [at] umfcd.ro.

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Dataset file: 2.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_December_2020.csv

Dataset graphical preview: 2.0.0.INFOGRAPHIC_CloudFraction_vs_COVID-19_mortality_Europe_March-December_2020.png

DATASET: 444 rows (records), with the following fields:

"Country" : Country name; 37 European countries included.

"Date": Date stamp at the collection time. Data collection was performed in the last day of every month. Date format: YYYY-MM-DD

"Month_Key" : Date stamp at the collection time, formatted for easier monthly time series analysis. Date format: YYYY-MM

"Month_Fct2020" Date stamp at the collection time,formatted for easier graphing, as a string with names of the months (in English).

"Deaths_per_1Mpop" : Monthly mortality from COVID-19 raported in the country, reported as number of COVID-19 deaths per 1 million population of the country, in that particular month / country. NB: it is reported as million population, not patients.

"LogDeaths_per_1Mpop" : Log10 transformation of "Deaths_per_1Mpop"

"Insolation_Average" : Insolation average (solar irradiance at ground level), in that particular month / country. It is expressed in Watt / square meter of the ground surface. Data derived from data avaialble at NASA Langley Research Center, NASA’s Earth Observatory, CERES / FLASHFlux team, 2020, https://neo.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M (old link: https://neo.sci.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M )

"Cloud_Fraction" : Cloudiness (also known as cloud fraction, cloud cover, cloud amount or sky cover), as decimal fraction of the sky obscured by clouds, in that particular month / country. Data derived from NASA Goddard Space Flight Center, NASA’s Earth Observatory, MODIS Atmosphere Science Team, 2020, https://neo.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR (old link: https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR )

"CENTR_latitude" and "CENTR_longitude" : Latitude and Longitude of the country centroid, for each country. Data derived from Google LLC, "Dataset publishing language: country centroids", https://developers.google.com/public-data/docs/canonical/countries_csv
NOTE: This is identical in every month (obviuously); it is redundantly included for easier monthly sectional analysis of the data.

===========

Versioning of the dataset: MAJOR: changes yearly; 1 = 2020 MINOR: changes if new monthly data is added in that particular year. PATCH: Changes only if errors or minor edits were performed.

===========

CHANGELOG:

Version 2.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_December_2020.csv - CERES/FLASHFLUX data for August-December 2020 became available at new links at nasa.gov - These data were gathered, analyzed and introduced in this dataset (2.0.0). - updated links for CERES/FLASHFLUX and MODIS dataset - added DOI link for preprint - minor edits on text. -Dataset file source for this version (internal analysis source file): db_covid_all-ANALYSIS.2020-all-year_versiunea18d.csv

Version 1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv First version Dataset file source for this version (internal analysis source file): db_covid_all-ANALYSIS.2020-09-22_r10.csv

Louisville Metro Public Health and Wellness has a Testing Taskforce (now combined with the Vaccine Taskforce) pulling together labs and healthcare systems in Jefferson County to coordinate COVID-19 testing efforts and response across the country.Data Dictionary Field Name Field Type Field Description

ID Integer Unique identifier

Date Date Data the tests were reported.

Centers Text Center the tests were conducted.

No of Tests Integer No of test as the reported date

Address Text address of the testing center

Council District Integer council district where the testing center belong to

Zip Code Text Zip code of the testing center

ABSTRACT Background : The Covid-19 pandemic associated with the SARS-CoV-2 has caused very high death tolls in many countries, while it has had less prevalence in other countries of Africa and Asia. Climate and geographic conditions, as well as other epidemiologic and demographic conditions, were a matter of debate on whether or not they could have an effect on the prevalence of Covid-19. Objective : In the present work, we sought a possible relevance of the geographic location of a given country on its Covid-19 prevalence. On the other hand, we sought a possible relation between the history of epidemiologic and demographic conditions of the populations and the prevalence of Covid-19 across four continents (America, Europe, Africa, and Asia). We also searched for a possible impact of pre-pandemic alcohol consumption in each country on the two year death tolls across the four continents. Methods : We have sought the death toll caused by Covid-19 in 39 countries and obtained the registered deaths from specialized web pages. For every country in the study, we have analysed the correlation of the Covid-19 death numbers with its geographic latitude, and its associated climate conditions, such as the mean annual temperature, the average annual sunshine hours, and the average annual UV index. We also analyzed the correlation of the Covid-19 death numbers with epidemiologic conditions such as cancer score and Alzheimer score, and with demographic parameters such as birth rate, mortality rate, fertility rate, and the percentage of people aged 65 and above. In regard to consumption habits, we searched for a possible relation between alcohol intake levels per capita and the Covid-19 death numbers in each country. Correlation factors and determination factors, as well as analyses by simple linear regression and polynomial regression, were calculated or obtained by Microsoft Exell software (2016). Results : In the present study, higher numbers of deaths related to Covid-19 pandemic were registered in many countries in Europe and America compared to other countries in Africa and Asia. The analysis by polynomial regression generated an inverted bell-shaped curve and a significant correlation between the Covid-19 death numbers and the geographic latitude of each country in our study. Higher death numbers were registered in the higher geographic latitudes of both hemispheres, while lower scores of deaths were registered in countries located around the equator line. In a bell shaped curve, the latitude levels were negatively correlated to the average annual levels (last 10 years) of temperatures, sunshine hours, and UV index of each country, with the highest scores of each climate parameter being registered around the equator line, while lower levels of temperature, sunshine hours, and UV index were registered in higher latitude countries. In addition, the linear regression analysis showed that the Covid-19 death numbers registered in the 39 countries of our study were negatively correlated with the three climate factors of our study, with the temperature as the main negatively correlated factor with Covid-19 deaths. On the other hand, cancer and Alzheimer's disease scores, as well as advanced age and alcohol intake, were positively correlated to Covid-19 deaths, and inverted bell-shaped curves were obtained when expressing the above parameters against a country’s latitude. Instead, the (birth rate/mortality rate) ratio and fertility rate were negatively correlated to Covid-19 deaths, and their values gave bell-shaped curves when expressed against a country’s latitude. Conclusion : The results of the present study prove that the climate parameters and history of epidemiologic and demographic conditions as well as nutrition habits are very correlated with Covid-19 prevalence. The results of the present study prove that low levels of temperature, sunshine hours, and UV index, as well as negative epidemiologic and demographic conditions and high scores of alcohol intake may worsen Covid-19 prevalence in many countries of the northern hemisphere, and this phenomenon could explain their high Covid-19 death tolls. Keywords : Covid-19, Coronavirus, SARS-CoV-2, climate, temperature, sunshine hours, UV index, cancer, Alzheimer disease, alcohol.

Content

This was based on the COVID19 Global Forecasting (Week 3) competition data, with data added from the following datasets:

• Coronavirus Lat/Lon Dataset
• COVID-19 containment and mitigation measures (future)
• Population by Country - 2020
• World Bank WDI 2.12 - Health Systems

All missing data is filled.

Inspiration

I hope this dataset helps anyone trying to get answers about this virus, really do.

ABSTRACT Background : The Covid-19 pandemic has caused very high death tolls across the world in the last two years. Geographic latitude, climate factors, and other human related conditions such as epidemiologic and demographic history are taught to have played a role in the prevalence of Covid-19. Objective : This observational study aimed to investigate possible relations between geographic latitude-associated climate factors and Covid-19 death numbers in 29 countries. The study also aimed to investigate the relationship between geographic latitude and the history of epidemiologic (cancer, Alzheimer's disease) and demographic factors (birth rate, mortality rate, fertility rate, people aged 65 and over), as well as alcohol intake habits. And finally, the study also aimed to evaluate the relationships between epidemiologic and demographic factors, as well as alcohol intake habits with Covid-19 deaths. Methods : We sought the Covid-19 death toll in 29 countries in Europe, Africa, and the Middle East (located in both hemispheres and between the meridian lines "-15°" and "+50°"). We obtained the death numbers for Covid-19 and other geographic (latitude, longitude) and climate factors (average annual temperature, sunshine hours, and UV index) and epidemiologic and demographic parameters as well as data on alcohol intake per capita from official web pages. Based on records of epidemiologic and demographic history, and alcohol intake data, we have calculated a General Immune Capacity (GIC) score for each country. Geographic latitude and climate factors were plotted against each of Covid-19 death numbers, epidemiologic and demographic parameters, and alcohol intake per capita. Data was analysed by simple linear regression or polynomial regression. All statistical data was collected using Microsoft Excell software (2016). Results : Our observational study found higher death numbers in the higher geographic latitudes of both hemispheres, while lower scores of deaths were registered in countries located around the equator line and low latitudes. When the Covid-19 death numbers were plotted against the geographic latitude of each country, an inverted bell-shaped curve was obtained (coefficient of determination R2=0.553). In contrast, bell-shaped curves were obtained when latitude was plotted against annual average temperature (coefficient of determination R2= 0.91), average annual sunshine hours (coefficient of determination R2= 0.79) and average annual UV index (coefficient of determination R2= 0.89). In addition, plotting the latitude of each country against the General Immune Capacity score of each country gave an inverted bell-shaped curve (coefficient of determination R2=0.755). Linear regression analysis of the General Immune Capacity score of each country and its Covid-19 deaths showed a very significant negative correlation (coefficient of determination R² = 0,71, p=6.79x10-9). Linear regression analysis of the Covid-19 death number plotted against the average annual temperature temperature and the average annual sunshine hours or the average annual UV index gave very significant negative correlations with the following coefficients of determination: (R2 = 0.69, p = 1.94x10-8), (R2 = 0.536, p = 6.31x10-6) and (R2 = 0.599, p = 8.30x10-7), respectively. Linear regression analysis of the General Immune Capacity score of each country plotted against its average annual temperature temperature and the average annual sunshine hours or the average annual UV index gave very significant negative correlations, with the following coefficients of determination: (R2 = 0.86, p = 3.63x10-13), (R2 = 0.69, p = 2.18x10-8) and (R2 = 0.77, p= 2.47x10-10), respectively. Conclusion : The results of the present study prove that at certain geographic latitudes and their three associated climate parameters are negatively correlated to Covid-19 mortality. On the other hand, our data showed that the General Immune Capacity score, which includes many human related parameters, is inversely correlated to Covid-19 mortality. Likewise, geographic location and health and demographic history were key elements in the prevalence of the Covid-19 pandemic in a given country. On the other hand, the study points to the possible protective role of UV light against Covid-19. The therapeutic potential of UV light against the Covid-19 associated with SARS-Cov-2 is discussed.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This feature layer contains the most up-to-date COVID-19 cases for the US and Canada. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by the Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact Johns Hopkins.IMPORTANT NOTICE: 1. Fields for Active Cases and Recovered Cases are set to 0 in all locations. John Hopkins has not found a reliable source for this information at the county level but will continue to look and carry the fields.2. Fields for Incident Rate and People Tested are placeholders for when this becomes available at the county level.3. In some instances, cases have not been assigned a location at the county scale. those are still assigned a state but are listed as unassigned and given a Lat Long of 0,0.Data Field Descriptions by Alias Name:Province/State: (Text) Country Province or State Name (Level 2 Key)Country/Region: (Text) Country or Region Name (Level 1 Key)Last Update: (Datetime) Last data update Date/Time in UTCLatitude: (Float) Geographic Latitude in Decimal Degrees (WGS1984)Longitude: (Float) Geographic Longitude in Decimal Degrees (WGS1984)Confirmed: (Long) Best collected count of Confirmed Cases reported by geographyRecovered: (Long) Not Currently in Use, JHU is looking for a sourceDeaths: (Long) Best collected count for Case Deaths reported by geographyActive: (Long) Confirmed - Recovered - Deaths (computed) Not Currently in Use due to lack of Recovered dataCounty: (Text) US County Name (Level 3 Key)FIPS: (Text) US State/County CodesCombined Key: (Text) Comma separated concatenation of Key Field values (L3, L2, L1)Incident Rate: (Long) People Tested: (Long) Not Currently in Use Placeholder for additional dataPeople Hospitalized: (Long) Not Currently in Use Placeholder for additional data

Updated @ March 13, 2020

ver 0.0.12

• added additional data since last update

ver 0.0.11

• added Lat / Lon / Country Code / Region / Country Flag (image URL)
• cleaned timestamp format

Context

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. (source: CDC)

In this dataset, you will have minutes-level timesereis 2019-nCoV reporting data which can help capture the outbreak trend more accurately than the daily data.

Content

• Available File Format

  • CSV

• Time Window

  • ~0.5 Hour (may have some gaps in early mornings)

• Date Range

  • 2020-01-22 ~ 2020-03-11 (actively updating)

• Geographic Region

  • The Greater China Area (China Mainland, Hong Kong, Macau, and Taiwan)
  • The worldwide impacted areas

• Columns

  • province: String, the reported provinces / areas (not listed if no cases reported).
  • country: the country name.
  • latitude: the latitude data of the country.
  • longitude : the longitude data of the country.
  • confirmed_cases: Int, the number of confirmed cases of the place at the reporting time.
  • deaths: Int, the number of deaths of the place at the reporting time.
  • recovered, Int, the number of recovered patients at the reporting time.
  • update_time: Timestamp (CST timezone), the reporting timestamp.
  • data_source: String, the raw data sources (currently bno and dxy).
  • country_code: String, this is the country code.
  • region: String, this is the region (Europe, Asia etc.).
  • country_flag: String, this is the URL for country flag image.

Acknowledgements

Special thanks to @globalcitizen who has scrapped the raw data files from multiple public sources.

Repo here ==> https://github.com/globalcitizen/2019-wuhan-coronavirus-data

• Original Data Sources:
  • DXY.cn(Chinese medical information site)
  • bnownews.com
  • JHU

Please contact me if you consider this dataset violate your copyright and I'm happy to remove it.

Inspiration

• To the whole Kaggle community:
  • From this provided dataset, how do you see the outbreak trend of 2019-nCoV different from the historical coronavirus outbreaks (e.g. SARS, MERS)?
  • What additional dataset do you require so you can get better insights about 2019-nCov?

UPVOTES ==> Let more people know this dataset and use it to gather insights.

Appreciate it Thanks

In the face of the COVID-19 pandemic, understanding the interplay between environmental factors and virus spread is crucial for global preparedness strategies. This study explores how geographic latitude, sunshine duration, and vitamin D status were associated with the incidence and fatality rates of COVID-19 across 187 countries during the crucial early months of the outbreak. Data on the total number of COVID-19 cases by country were obtained from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) as of June 30, 2020. Univariate and multivariate regression analyses were conducted to determine the associations between COVID-19 cases and latitude, average hours of sunshine from January to June, and mean 25-hydroxyvitamin D (25(OH)D) levels. The average COVID-19 cumulative incidence and mortality per million population were 2,087 and 69, respectively, with a case fatality rate of 3.19%. COVID-19 case fatality rate was positively associated with latitude (β = 0.030; 95% CI: 0.008, 0.052) and negatively associated with hours of sunshine (β = -1.51; 95% CI: -4.44, 1.41) and 25(OH)D levels (β = -0.054; 95% CI: -0.089, -0.019) in adjusted linear regression analyses. Findings were similar for COVID-19 cumulative incidence and mortality rate. These findings indicate that higher latitude and lower 25(OH)D levels were associated with increased COVID-19 severity and mortality. While the data highlight potential links between vitamin D status and COVID-19 outcomes, causality cannot be inferred.

Re-gridded TROPOMI NO2 data at 0.01 x 0.01 degree resolution.First file is a March 13 - June 13, 2019 average representing the "pre-COVID-19" baselineSecond file is a March 13 - June 13, 2020 average representing the"post-COVID-19" lockdown.Latitude and Longitude file is also included. Each latitude and longitude value represents the center of the gridbox. Each gridbox edge is offset by 0.005 degrees,

ABSTRACT Background: Following two years of the Covid-19 pandemic, thousands of deaths were registered around the world, however, death tolls differed from a country to another. A question on whether climate parameters in each country could or not affects coronavirus incidence and Covid-19 death toll is under debate. Objective: In the present work, it is aimed to check the numbers of deaths caused by Covid-19 in 39 countries of four continents (America, Europe, Africa and Asia), and to analyse their possible correlation with climate parameters in a given country, such as the mean of annual temperature, the annual average sunshine hours and the annual average UV index in each country. Methods: We have sought the deaths number caused by Covid-19 in 39 countries and have analysed its correlation degree with the mean annual temperature, the average annual sunshine hours and the average annual UV index. Correlation and determination factors were obtained by Microsoft Exell software (2016). Results: In the present study, higher numbers of deaths related to Covid-19 were registered in many countries of Europe and America compared to other countries in Africa and Asia. On the other hand, after both the first year and the second year of the pandemic, the death numbers registered in the 39 countries of our study were very negatively correlated with the three climate factors of our study, namely, annual average temperature, sunshine hours and UV index. Conclusion:The results of the present study prove that the above climate parameters may have some kind of influence on the coronavirus incidence through a yet unknown mechanism. Our data support the hypothesis that countries which have elevated annual temperatures and elevated sunshine hours may be less vulnerable to the coronavirus SARS-CoV-2 and to its associated Covid-19 disease. Countries with the above characteristics have also elevated levels of average annual UV rays that might play a key role against the spread of the coronavirus.Thus, geographical latitude and longitude of a given country could have been the key points for the outcome of virus incidence and Covid-19 spread around the globe during the past two years. The results prove that elevated levels of temperature, sunshine hours and UV index could play a protective effect against the coronavirus, although their mechanisms of action are still unknown.

Extract from raw data from “It remains open”: list of places open or closed during the confinement period. To edit the data, you can contribute directly to OpenStreetMap or It remains open, all contribution info is here. The file is in CSV format (separated by commas), with UTF-8 encoding. It is updated every hour. The data structure is as follows: * osm_id: OpenStreetMap identifier of the place * name: name of the place * cat: category (office tags, shop, craft, amenity from OpenStreetMap) * brand: name of the sign/network * Wikidata: Wikidata ID associated with the sign * url_hours: URL link to which the business schedules of the associated sign are entered * info: free text to give more details on access conditions * status: state of opening or closure. Values: open = as usual, open_adapted = hours likely to have changed, partial = potentially closed place, closed = closed place. * opening_hours: opening hours during containment (see OSM wiki) * lon: longitude (WGS84, decimal degrees) * Lat: latitude (WGS84, decimal degrees)

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.MRI.MRI-ESM2-0.ssp245-covid' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MRI-ESM2.0 climate model, released in 2017, includes the following components: aerosol: MASINGAR mk2r4 (TL95; 192 x 96 longitude/latitude; 80 levels; top level 0.01 hPa), atmos: MRI-AGCM3.5 (TL159; 320 x 160 longitude/latitude; 80 levels; top level 0.01 hPa), atmosChem: MRI-CCM2.1 (T42; 128 x 64 longitude/latitude; 80 levels; top level 0.01 hPa), land: HAL 1.0, ocean: MRI.COM4.4 (tripolar primarily 0.5 deg latitude/1 deg longitude with meridional refinement down to 0.3 deg within 10 degrees north and south of the equator; 360 x 364 longitude/latitude; 61 levels; top grid cell 0-2 m), ocnBgchem: MRI.COM4.4, seaIce: MRI.COM4.4. The model was run by the Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan (MRI) in native nominal resolutions: aerosol: 250 km, atmos: 100 km, atmosChem: 250 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

This table is merged through FIPS of US counties, and also includes other selected output parameters from SaTScan such as p-values, LOC_RR (location or county relative risk), CLU_RR (cluster relative risk), LOC_LAT (location latitude), LOC_LONG (location longitude). (XLSX)

Dataset

This dataset was created by Fabio.vaz

Contents

These data include all datasets published for 'CMIP6.DAMIP.MRI.MRI-ESM2-0.ssp245-covid' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MRI-ESM2.0 climate model, released in 2017, includes the following components: aerosol: MASINGAR mk2r4 (TL95; 192 x 96 longitude/latitude; 80 levels; top level 0.01 hPa), atmos: MRI-AGCM3.5 (TL159; 320 x 160 longitude/latitude; 80 levels; top level 0.01 hPa), atmosChem: MRI-CCM2.1 (T42; 128 x 64 longitude/latitude; 80 levels; top level 0.01 hPa), land: HAL 1.0, ocean: MRI.COM4.4 (tripolar primarily 0.5 deg latitude/1 deg longitude with meridional refinement down to 0.3 deg within 10 degrees north and south of the equator; 360 x 364 longitude/latitude; 61 levels; top grid cell 0-2 m), ocnBgchem: MRI.COM4.4, seaIce: MRI.COM4.4. The model was run by the Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan (MRI) in native nominal resolutions: aerosol: 250 km, atmos: 100 km, atmosChem: 250 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Individuals using the data must abide by terms of use for CMIP6 data (https://pcmdi.llnl.gov/CMIP6/TermsOfUse). The original license restrictions on these datasets were recorded as global attributes in the data files, but these may have been subsequently updated.

This feature layer contains the most up-to-date COVID-19 cases for the US, Canada. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. The China data is automatically updating at least once per hour, and non China data is updating manually. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact Johns Hopkins.IMPORTANT NOTICE: 1. Fields for Active Cases and Recovered Cases are set to 0 in all locations. John Hopkins has not found a reliable source for this information at the county level but will continue to look and carry the fields.2. Fields for Incident Rate and People Tested are placeholders for when this becomes available at the county level.3. In some instances, cases have not been assigned a location at the county scale. those are still assigned a state but are listed as unassigned and given a Lat Long of 0,0.